Analyzing　road　risks　and　developing　targeted　countermeasures　are　essential　for　a　safe　and　orderly　traffic　flow.　However,　previous　intersection　safety　analyses　were　conducted　based　on　crash　data.　Little　research　has　been　conducted　on　surrogate　safety　measures　based　on　risky　driving　behavior.　In　this　study,　categorical　boosting　(CatBoost)　and　Shapley　additive　explanation　(SHAP)　were　used　to　analyze　the　impact　of　features　on　traffic　order　using　a　set　of　multisource　data　that　include　roadway　geometry,　signal　control,　and　land　use.　The　traffic　data　for　intersection　entrances　in　Beijing　were　collected　from　navigation　systems,　field　investigations,　and　application　programming　interfaces.　The　model　results　showed　that　CatBoost　exhibits　a　prediction　accuracy　of　83.5%,　a　recall　of　83.5%,　and　an　F1　score　of　81.1%.　Moreover,　the　importance,　total　effects,　main　effects,　and　interaction　effects　of　influence　factors　were　analyzed　by　using　SHAP.　It　was　found　that　the　congestion　index　(CI)　has　significant　negative　effects　on　traffic　order.　A　larger　number　of　lanes　and　more　electronic　traffic　control　were　found　to　have　a　positive　effect　on　traffic　order.　Intersection　entrances　with　three-phase　signals　or　off-peak　intersection　entrances　helped　increase　traffic　order.　Moreover,　a　high　green　ratio　for　through　vehicles　can　reduce　the　positive　impact　of　CI　on　traffic　order　when　the　value　of　CI　is　1.1-1.4,　and　the　signal　control　scheme　with　a　high　left-turn　green　ratio　would　result　in　a　safe　and　orderly　traffic　flow.　The　results　from　this　study　can　be　used　for　further　studies　on　improving　traffic　safety　at　signalized　intersections.　©　2023　American　Society　of　Civil　Engineers.